fig: timeerror shows the distribution of computation time vs. VI estimation accuracy for three different groups of variables (important and correlated, important and uncorrelated, unimportant and uncorrelated). We see that LazyVI and retrain are both accurate across all groups of variables, but LazyVI is much faster. Dropout is consistently the fastest method, but is highly inaccurate in estimating \(\text{\sc vi}\)  for the first group of variables due to their strong correlations. Also in fig: timeerror we show the empirical coverage of the LazyVI and retrain 95% confidence intervals. We see that both retrain and LazyVI achieve desirable coverage for the three important variables; poor coverage of unimportant variables is expected and possibly remedied with a sample-splitting procedure [1]}.
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